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  • 1
    CTGAN

    CTGAN

    Conditional GAN for generating synthetic tabular data

    ...The SDV library provides wrappers for preprocessing your data as well as additional usability features like constraints. When using the CTGAN library directly, you may need to manually preprocess your data into the correct format, for example, continuous data must be represented as floats. Discrete data must be represented as ints or strings. The data should not contain any missing values.
    Downloads: 8 This Week
    Last Update:
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  • 2
    YData Synthetic

    YData Synthetic

    Synthetic data generators for tabular and time-series data

    ...This repository contains material related to Generative Adversarial Networks for synthetic data generation, in particular regular tabular data and time-series. It consists a set of different GANs architectures developed using Tensorflow 2.0. Several example Jupyter Notebooks and Python scripts are included, to show how to use the different architectures. YData synthetic has now a UI interface to guide you through the steps and inputs to generate structure tabular data. The streamlit app is available form v1.0.0 onwards.
    Downloads: 0 This Week
    Last Update:
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  • 3
    Reliable Metrics for Generative Models

    Reliable Metrics for Generative Models

    Code base for the precision, recall, density, and coverage metrics

    ...Because it does not differentiate the fidelity and diversity aspects of the generated images, recent papers have introduced variants of precision and recall metrics to diagnose those properties separately. In this paper, we show that even the latest version of the precision and recall (Kynkäänniemi et al., 2019) metrics are not reliable yet. For example, they fail to detect the match between two identical distributions, they are not robust against outliers, and the evaluation hyperparameters are selected arbitrarily. We propose density and coverage metrics that solve the above issues.
    Downloads: 0 This Week
    Last Update:
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